Chatbot.ai Chatbot: A RAG System with Authentication and RBAC 🤖

This project is a full-stack, retrieval-augmented generation (RAG) chatbot designed to answer questions based on a specific set of documents. It features a secure backend API built with FastAPI and an interactive frontend created with Streamlit.

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Key Features ✨

• Retrieval-Augmented Generation (RAG): The chatbot retrieves relevant information from a vector database (ChromaDB) before generating an answer using Google's Gemini LLM, ensuring responses are grounded in your data.
• User Authentication: A secure login system using JWT (JSON Web Tokens) to protect the API.
• Role-Based Access Control (RBAC): Users can only query documents relevant to their assigned role (e.g., HR, Finance, Tech), preventing unauthorized data access.
• Intelligent Routing: The API has two distinct endpoints to handle different user types, ensuring a clean and secure design.
• Hybrid Logic: The chatbot can answer generic questions (like "What is the time?") without needing to perform a database search.
• Interactive Frontend: A modern, clean user interface built with Streamlit for a great chat experience.

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Project Structure 📂

.
├── .env                  # Environment variables and API keys
├── app.py                # FastAPI backend server
├── auth.py               # Authentication and JWT logic
├── config.py             # Global configuration settings
├── ingest.py             # Script to load documents into the vector database
├── streamlit_app.py      # Streamlit frontend application
├── users.py              # Mock user data for development
├── utils.py              # Utility functions for the project
└── sample_docs/          # Folder containing source documents for ingestion
    ├── finance_budget.txt
    ├── hr_policies.txt
    └── tech_onboarding.txt

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Prerequisites 🛠️

Before running the application, make sure you have Python 3.9+ installed.

• API Keys:
  • A Google Gemini API Key for the LLM. You can get one from Google AI Studio.
  • A JWT Secret Key. This is a long, random string you create yourself.
• A virtual environment: Recommended to keep dependencies isolated.

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Installation and Setup 🚀

1. Clone the project files: Create the folder structure above and place the provided code into each file.
2. Install dependencies: Navigate to your project directory in the terminal and run:

   pip install fastapi "uvicorn[standard]" langchain-chroma langchain-huggingface langchain-google-genai python-dotenv "passlib[bcrypt]" "python-jose[cryptography]" streamlit requests 

3. Set up the .env file: Create a file named .env in your project's root directory and add your API keys.

   GEMINI_API_KEY="your-gemini-api-key-here"
   JWT_SECRET_KEY="a-very-long-and-random-string-for-jwt-signing"

4. Ingest Documents: Run the ingestion script to populate your vector database. This only needs to be done once.

   python ingest.py

5. Run the virtual environment first: Run the virtual environmet by using

   ./.venv/Scripts/Activate

6. Run the FastAPI Backend: In your first terminal, start the backend server.

   uvicorn app:app --reload

7. Run the Streamlit Frontend: In a separate terminal, start the frontend application.

   streamlit run streamlit_app.py

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Usage 💬

Access the application by navigating to http://localhost:8501 in your web browser. You can log in with the following mock credentials:

Username	Password	Role
radarshi	admin001	admin
alice	alice004	hr
shyam	shyam003	finance
kartik	kartik002	tech

• Regular Users (e.g., hr): After logging in, you can only ask questions about HR-related documents.
• Admin User: After logging in, you can query documents from any role by selecting it from the sidebar. You can also ask generic questions like "What is the time?".